An Automatic Stock Market Forecasting and Portfolio Selection Mechanism Based on VPRS, ARX and Grey System

In this study, a different approach by the fuzzy C-means and relevant Fuzzy theories to extract the threshold value beta of VPRS applied to continuous information systems is presented. Then the variable precision rough set (VPRS) theory is combined with moving average autoregressive exogenous (ARX) prediction model and grey systems theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed approach, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data is then reduced using a GM(1,N) model, clustered using a Fuzzy C-means clustering algorithm and then supplied to a VPRS classification module which selects appropriate investment stocks by applying a set of decision-making rules. Finally, a grey relational analysis technique is employed to specify an appropriate weighting of the selected stocks such that the portfoliopsilas rate of return is maximized. It is found that the proposed method yields an average annual rate of return, 29.33%, on the selected stocks from 2004 to 2006 in Taiwan stock market.